Overview

Dataset statistics

Number of variables16
Number of observations38821
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.0 MiB
Average record size in memory163.2 B

Variable types

Numeric10
Categorical6

Alerts

name has a high cardinality: 38244 distinct valuesHigh cardinality
host_name has a high cardinality: 9885 distinct valuesHigh cardinality
neighbourhood has a high cardinality: 218 distinct valuesHigh cardinality
last_review has a high cardinality: 1764 distinct valuesHigh cardinality
id is highly overall correlated with host_idHigh correlation
host_id is highly overall correlated with idHigh correlation
latitude is highly overall correlated with neighbourhood_groupHigh correlation
longitude is highly overall correlated with neighbourhood_groupHigh correlation
number_of_reviews is highly overall correlated with reviews_per_monthHigh correlation
reviews_per_month is highly overall correlated with number_of_reviewsHigh correlation
neighbourhood_group is highly overall correlated with latitude and 1 other fieldsHigh correlation
price is highly skewed (γ1 = 23.6735943)Skewed
minimum_nights is highly skewed (γ1 = 27.54218703)Skewed
name is uniformly distributedUniform
id has unique valuesUnique
availability_365 has 12675 (32.6%) zerosZeros

Reproduction

Analysis started2023-02-26 11:54:58.138611
Analysis finished2023-02-26 11:55:10.942395
Duration12.8 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct38821
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18100808
Minimum2539
Maximum36455809
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size606.6 KiB
2023-02-26T17:25:11.020904image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2539
5-th percentile905425
Q18721444
median18872858
Q327567455
95-th percentile34337649
Maximum36455809
Range36453270
Interquartile range (IQR)18846011

Descriptive statistics

Standard deviation10693722
Coefficient of variation (CV)0.59078701
Kurtosis-1.2036177
Mean18100808
Median Absolute Deviation (MAD)9404082
Skewness-0.071215349
Sum7.0269148 × 1011
Variance1.143557 × 1014
MonotonicityStrictly increasing
2023-02-26T17:25:11.127264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2539 1
 
< 0.1%
24082591 1
 
< 0.1%
24083340 1
 
< 0.1%
24083686 1
 
< 0.1%
24083746 1
 
< 0.1%
24085451 1
 
< 0.1%
24086178 1
 
< 0.1%
24086457 1
 
< 0.1%
24086562 1
 
< 0.1%
24086704 1
 
< 0.1%
Other values (38811) 38811
> 99.9%
ValueCountFrequency (%)
2539 1
< 0.1%
2595 1
< 0.1%
3831 1
< 0.1%
5022 1
< 0.1%
5099 1
< 0.1%
5121 1
< 0.1%
5178 1
< 0.1%
5203 1
< 0.1%
5238 1
< 0.1%
5295 1
< 0.1%
ValueCountFrequency (%)
36455809 1
< 0.1%
36442252 1
< 0.1%
36438336 1
< 0.1%
36427429 1
< 0.1%
36425863 1
< 0.1%
36413632 1
< 0.1%
36411407 1
< 0.1%
36390226 1
< 0.1%
36351543 1
< 0.1%
36351128 1
< 0.1%

name
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct38244
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Memory size606.6 KiB
Home away from home
 
12
Loft Suite @ The Box House Hotel
 
11
Private Room
 
10
#NAME?
 
10
Brooklyn Apartment
 
9
Other values (38239)
38769 

Length

Max length161
Median length73
Mean length36.975245
Min length1

Characters and Unicode

Total characters1435416
Distinct characters677
Distinct categories20 ?
Distinct scripts11 ?
Distinct blocks17 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37852 ?
Unique (%)97.5%

Sample

1st rowClean & quiet apt home by the park
2nd rowSkylit Midtown Castle
3rd rowCozy Entire Floor of Brownstone
4th rowEntire Apt: Spacious Studio/Loft by central park
5th rowLarge Cozy 1 BR Apartment In Midtown East

Common Values

ValueCountFrequency (%)
Home away from home 12
 
< 0.1%
Loft Suite @ The Box House Hotel 11
 
< 0.1%
Private Room 10
 
< 0.1%
#NAME? 10
 
< 0.1%
Brooklyn Apartment 9
 
< 0.1%
Private room 8
 
< 0.1%
Cozy Brooklyn Apartment 8
 
< 0.1%
New york Multi-unit building 8
 
< 0.1%
Hillside Hotel 7
 
< 0.1%
Harlem Gem 7
 
< 0.1%
Other values (38234) 38731
99.8%

Length

2023-02-26T17:25:11.446260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
in 13290
 
5.6%
room 8060
 
3.4%
6622
 
2.8%
bedroom 5951
 
2.5%
private 5943
 
2.5%
apartment 5317
 
2.2%
cozy 4161
 
1.8%
apt 3716
 
1.6%
brooklyn 3365
 
1.4%
to 3213
 
1.4%
Other values (10708) 177579
74.9%

Most occurring characters

ValueCountFrequency (%)
199762
 
13.9%
e 98850
 
6.9%
o 97484
 
6.8%
t 84336
 
5.9%
a 82792
 
5.8%
r 77952
 
5.4%
i 75406
 
5.3%
n 75298
 
5.2%
l 41039
 
2.9%
m 39024
 
2.7%
Other values (667) 563473
39.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 959266
66.8%
Uppercase Letter 216415
 
15.1%
Space Separator 199764
 
13.9%
Other Punctuation 27473
 
1.9%
Decimal Number 19365
 
1.3%
Dash Punctuation 5499
 
0.4%
Other Letter 1894
 
0.1%
Math Symbol 1884
 
0.1%
Close Punctuation 1265
 
0.1%
Open Punctuation 1143
 
0.1%
Other values (10) 1448
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
70
 
3.7%
38
 
2.0%
37
 
2.0%
35
 
1.8%
34
 
1.8%
34
 
1.8%
32
 
1.7%
29
 
1.5%
23
 
1.2%
23
 
1.2%
Other values (438) 1539
81.3%
Lowercase Letter
ValueCountFrequency (%)
e 98850
 
10.3%
o 97484
 
10.2%
t 84336
 
8.8%
a 82792
 
8.6%
r 77952
 
8.1%
i 75406
 
7.9%
n 75298
 
7.8%
l 41039
 
4.3%
m 39024
 
4.1%
s 38348
 
4.0%
Other values (52) 248737
25.9%
Other Symbol
ValueCountFrequency (%)
232
31.9%
154
21.2%
100
13.7%
34
 
4.7%
32
 
4.4%
17
 
2.3%
13
 
1.8%
12
 
1.6%
11
 
1.5%
8
 
1.1%
Other values (44) 115
15.8%
Uppercase Letter
ValueCountFrequency (%)
B 23894
 
11.0%
S 20958
 
9.7%
C 17108
 
7.9%
A 15730
 
7.3%
R 14162
 
6.5%
P 11887
 
5.5%
E 11571
 
5.3%
L 11320
 
5.2%
M 9491
 
4.4%
N 9381
 
4.3%
Other values (28) 70913
32.8%
Other Punctuation
ValueCountFrequency (%)
, 7341
26.7%
! 6408
23.3%
/ 4070
14.8%
. 3560
13.0%
& 2719
 
9.9%
' 902
 
3.3%
* 833
 
3.0%
: 507
 
1.8%
# 468
 
1.7%
" 256
 
0.9%
Other values (11) 409
 
1.5%
Math Symbol
ValueCountFrequency (%)
+ 979
52.0%
| 635
33.7%
~ 194
 
10.3%
= 27
 
1.4%
> 22
 
1.2%
< 14
 
0.7%
6
 
0.3%
3
 
0.2%
2
 
0.1%
1
 
0.1%
Decimal Number
ValueCountFrequency (%)
1 6577
34.0%
2 5348
27.6%
3 1986
 
10.3%
5 1691
 
8.7%
0 1659
 
8.6%
4 967
 
5.0%
6 365
 
1.9%
7 317
 
1.6%
8 265
 
1.4%
9 190
 
1.0%
Close Punctuation
ValueCountFrequency (%)
) 1212
95.8%
] 35
 
2.8%
} 9
 
0.7%
6
 
0.5%
3
 
0.2%
Open Punctuation
ValueCountFrequency (%)
( 1091
95.5%
[ 34
 
3.0%
{ 9
 
0.8%
6
 
0.5%
3
 
0.3%
Dash Punctuation
ValueCountFrequency (%)
- 5438
98.9%
39
 
0.7%
21
 
0.4%
1
 
< 0.1%
Modifier Letter
ValueCountFrequency (%)
11
57.9%
6
31.6%
2
 
10.5%
Modifier Symbol
ValueCountFrequency (%)
^ 8
57.1%
´ 3
 
21.4%
` 3
 
21.4%
Space Separator
ValueCountFrequency (%)
199762
> 99.9%
  2
 
< 0.1%
Final Punctuation
ValueCountFrequency (%)
166
82.2%
36
 
17.8%
Nonspacing Mark
ValueCountFrequency (%)
154
92.2%
13
 
7.8%
Initial Punctuation
ValueCountFrequency (%)
38
84.4%
7
 
15.6%
Connector Punctuation
ValueCountFrequency (%)
_ 32
97.0%
1
 
3.0%
Control
ValueCountFrequency (%)
157
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 76
100.0%
Other Number
ValueCountFrequency (%)
² 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1175598
81.9%
Common 257674
 
18.0%
Han 1732
 
0.1%
Inherited 167
 
< 0.1%
Cyrillic 71
 
< 0.1%
Katakana 64
 
< 0.1%
Hangul 51
 
< 0.1%
Hiragana 34
 
< 0.1%
Georgian 13
 
< 0.1%
Hebrew 10
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
70
 
4.0%
38
 
2.2%
37
 
2.1%
35
 
2.0%
34
 
2.0%
34
 
2.0%
32
 
1.8%
29
 
1.7%
23
 
1.3%
23
 
1.3%
Other values (352) 1377
79.5%
Common
ValueCountFrequency (%)
199762
77.5%
, 7341
 
2.8%
1 6577
 
2.6%
! 6408
 
2.5%
- 5438
 
2.1%
2 5348
 
2.1%
/ 4070
 
1.6%
. 3560
 
1.4%
& 2719
 
1.1%
3 1986
 
0.8%
Other values (117) 14465
 
5.6%
Latin
ValueCountFrequency (%)
e 98850
 
8.4%
o 97484
 
8.3%
t 84336
 
7.2%
a 82792
 
7.0%
r 77952
 
6.6%
i 75406
 
6.4%
n 75298
 
6.4%
l 41039
 
3.5%
m 39024
 
3.3%
s 38348
 
3.3%
Other values (67) 465069
39.6%
Hangul
ValueCountFrequency (%)
6
 
11.8%
3
 
5.9%
3
 
5.9%
2
 
3.9%
2
 
3.9%
2
 
3.9%
2
 
3.9%
2
 
3.9%
2
 
3.9%
2
 
3.9%
Other values (24) 25
49.0%
Katakana
ValueCountFrequency (%)
8
 
12.5%
6
 
9.4%
5
 
7.8%
4
 
6.2%
4
 
6.2%
4
 
6.2%
3
 
4.7%
3
 
4.7%
3
 
4.7%
2
 
3.1%
Other values (18) 22
34.4%
Cyrillic
ValueCountFrequency (%)
о 7
 
9.9%
к 7
 
9.9%
а 7
 
9.9%
с 6
 
8.5%
р 5
 
7.0%
н 4
 
5.6%
е 4
 
5.6%
м 3
 
4.2%
и 3
 
4.2%
у 3
 
4.2%
Other values (13) 22
31.0%
Hiragana
ValueCountFrequency (%)
7
20.6%
4
11.8%
4
11.8%
4
11.8%
2
 
5.9%
2
 
5.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
Other values (7) 7
20.6%
Hebrew
ValueCountFrequency (%)
ע 2
20.0%
ב 2
20.0%
ר 2
20.0%
י 2
20.0%
ת 2
20.0%
Inherited
ValueCountFrequency (%)
154
92.2%
13
 
7.8%
Georgian
ValueCountFrequency (%)
13
100.0%
Devanagari
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1431964
99.8%
CJK 1732
 
0.1%
Misc Symbols 427
 
< 0.1%
Punctuation 368
 
< 0.1%
None 307
 
< 0.1%
Dingbats 248
 
< 0.1%
VS 167
 
< 0.1%
Cyrillic 71
 
< 0.1%
Hangul 51
 
< 0.1%
Hiragana 34
 
< 0.1%
Other values (7) 47
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
199762
 
14.0%
e 98850
 
6.9%
o 97484
 
6.8%
t 84336
 
5.9%
a 82792
 
5.8%
r 77952
 
5.4%
i 75406
 
5.3%
n 75298
 
5.3%
l 41039
 
2.9%
m 39024
 
2.7%
Other values (86) 560021
39.1%
Misc Symbols
ValueCountFrequency (%)
232
54.3%
100
23.4%
32
 
7.5%
12
 
2.8%
6
 
1.4%
6
 
1.4%
6
 
1.4%
4
 
0.9%
4
 
0.9%
4
 
0.9%
Other values (11) 21
 
4.9%
Punctuation
ValueCountFrequency (%)
166
45.1%
59
 
16.0%
39
 
10.6%
38
 
10.3%
36
 
9.8%
21
 
5.7%
7
 
1.9%
1
 
0.3%
1
 
0.3%
Dingbats
ValueCountFrequency (%)
154
62.1%
17
 
6.9%
13
 
5.2%
11
 
4.4%
8
 
3.2%
6
 
2.4%
5
 
2.0%
5
 
2.0%
4
 
1.6%
4
 
1.6%
Other values (10) 21
 
8.5%
VS
ValueCountFrequency (%)
154
92.2%
13
 
7.8%
CJK
ValueCountFrequency (%)
70
 
4.0%
38
 
2.2%
37
 
2.1%
35
 
2.0%
34
 
2.0%
34
 
2.0%
32
 
1.8%
29
 
1.7%
23
 
1.3%
23
 
1.3%
Other values (352) 1377
79.5%
None
ValueCountFrequency (%)
34
 
11.1%
à 25
 
8.1%
ó 18
 
5.9%
14
 
4.6%
é 14
 
4.6%
11
 
3.6%
· 11
 
3.6%
8
 
2.6%
ä 7
 
2.3%
² 7
 
2.3%
Other values (64) 158
51.5%
Georgian
ValueCountFrequency (%)
13
100.0%
Hiragana
ValueCountFrequency (%)
7
20.6%
4
11.8%
4
11.8%
4
11.8%
2
 
5.9%
2
 
5.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
Other values (7) 7
20.6%
Cyrillic
ValueCountFrequency (%)
о 7
 
9.9%
к 7
 
9.9%
а 7
 
9.9%
с 6
 
8.5%
р 5
 
7.0%
н 4
 
5.6%
е 4
 
5.6%
м 3
 
4.2%
и 3
 
4.2%
у 3
 
4.2%
Other values (13) 22
31.0%
Hangul
ValueCountFrequency (%)
6
 
11.8%
3
 
5.9%
3
 
5.9%
2
 
3.9%
2
 
3.9%
2
 
3.9%
2
 
3.9%
2
 
3.9%
2
 
3.9%
2
 
3.9%
Other values (24) 25
49.0%
Geometric Shapes
ValueCountFrequency (%)
4
40.0%
2
20.0%
2
20.0%
2
20.0%
Math Operators
ValueCountFrequency (%)
3
50.0%
2
33.3%
1
 
16.7%
Misc Technical
ValueCountFrequency (%)
2
40.0%
1
20.0%
1
20.0%
1
20.0%
Hebrew
ValueCountFrequency (%)
ע 2
20.0%
ב 2
20.0%
ר 2
20.0%
י 2
20.0%
ת 2
20.0%
Devanagari
ValueCountFrequency (%)
2
100.0%
Letterlike Symbols
ValueCountFrequency (%)
1
100.0%

host_id
Real number (ℝ)

Distinct30232
Distinct (%)77.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64245819
Minimum2438
Maximum2.7384167 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size606.6 KiB
2023-02-26T17:25:11.558282image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2438
5-th percentile709939
Q17029525
median28370925
Q31.0189055 × 108
95-th percentile2.3020517 × 108
Maximum2.7384167 × 108
Range2.7383923 × 108
Interquartile range (IQR)94861021

Descriptive statistics

Standard deviation75897516
Coefficient of variation (CV)1.1813612
Kurtosis0.30524294
Mean64245819
Median Absolute Deviation (MAD)25472582
Skewness1.2479603
Sum2.4940869 × 1012
Variance5.760433 × 1015
MonotonicityNot monotonic
2023-02-26T17:25:11.649271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
219517861 207
 
0.5%
61391963 79
 
0.2%
16098958 61
 
0.2%
137358866 51
 
0.1%
7503643 49
 
0.1%
190921808 46
 
0.1%
30283594 43
 
0.1%
1475015 42
 
0.1%
120762452 40
 
0.1%
2119276 39
 
0.1%
Other values (30222) 38164
98.3%
ValueCountFrequency (%)
2438 1
 
< 0.1%
2571 1
 
< 0.1%
2787 6
< 0.1%
2845 2
 
< 0.1%
2868 1
 
< 0.1%
2881 2
 
< 0.1%
3151 1
 
< 0.1%
3211 1
 
< 0.1%
3415 1
 
< 0.1%
3563 1
 
< 0.1%
ValueCountFrequency (%)
273841667 1
< 0.1%
273361532 1
< 0.1%
272872092 1
< 0.1%
272816114 1
< 0.1%
272557707 1
< 0.1%
272327753 1
< 0.1%
272314085 1
< 0.1%
272308792 1
< 0.1%
272241217 1
< 0.1%
271928929 1
< 0.1%

host_name
Categorical

Distinct9885
Distinct (%)25.5%
Missing0
Missing (%)0.0%
Memory size606.6 KiB
Michael
 
335
David
 
309
John
 
250
Alex
 
229
Sonder (NYC)
 
207
Other values (9880)
37491 

Length

Max length35
Median length31
Mean length6.1247005
Min length1

Characters and Unicode

Total characters237767
Distinct characters164
Distinct categories15 ?
Distinct scripts6 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6005 ?
Unique (%)15.5%

Sample

1st rowJohn
2nd rowJennifer
3rd rowLisaRoxanne
4th rowLaura
5th rowChris

Common Values

ValueCountFrequency (%)
Michael 335
 
0.9%
David 309
 
0.8%
John 250
 
0.6%
Alex 229
 
0.6%
Sonder (NYC) 207
 
0.5%
Sarah 179
 
0.5%
Maria 174
 
0.4%
Jessica 170
 
0.4%
Daniel 170
 
0.4%
Anna 159
 
0.4%
Other values (9875) 36639
94.4%

Length

2023-02-26T17:25:11.750936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
974
 
2.2%
and 580
 
1.3%
michael 375
 
0.9%
david 348
 
0.8%
john 288
 
0.7%
alex 277
 
0.6%
sonder 236
 
0.5%
nyc 215
 
0.5%
laura 214
 
0.5%
maria 206
 
0.5%
Other values (8860) 39883
91.5%

Most occurring characters

ValueCountFrequency (%)
a 30311
 
12.7%
e 22652
 
9.5%
i 19642
 
8.3%
n 19283
 
8.1%
r 13903
 
5.8%
l 12122
 
5.1%
o 9977
 
4.2%
s 7496
 
3.2%
t 7479
 
3.1%
h 7249
 
3.0%
Other values (154) 87653
36.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 186978
78.6%
Uppercase Letter 43660
 
18.4%
Space Separator 4871
 
2.0%
Other Punctuation 1403
 
0.6%
Open Punctuation 257
 
0.1%
Close Punctuation 255
 
0.1%
Dash Punctuation 176
 
0.1%
Decimal Number 65
 
< 0.1%
Other Letter 63
 
< 0.1%
Math Symbol 31
 
< 0.1%
Other values (5) 8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 30311
16.2%
e 22652
12.1%
i 19642
10.5%
n 19283
10.3%
r 13903
 
7.4%
l 12122
 
6.5%
o 9977
 
5.3%
s 7496
 
4.0%
t 7479
 
4.0%
h 7249
 
3.9%
Other values (49) 36864
19.7%
Other Letter
ValueCountFrequency (%)
6
 
9.5%
5
 
7.9%
5
 
7.9%
5
 
7.9%
3
 
4.8%
3
 
4.8%
2
 
3.2%
2
 
3.2%
1
 
1.6%
1
 
1.6%
Other values (30) 30
47.6%
Uppercase Letter
ValueCountFrequency (%)
A 5277
12.1%
J 4467
 
10.2%
M 4299
 
9.8%
S 3696
 
8.5%
C 2988
 
6.8%
L 2332
 
5.3%
D 2171
 
5.0%
R 2023
 
4.6%
K 1999
 
4.6%
E 1909
 
4.4%
Other values (23) 12499
28.6%
Other Punctuation
ValueCountFrequency (%)
& 1015
72.3%
. 274
 
19.5%
/ 38
 
2.7%
, 32
 
2.3%
' 23
 
1.6%
@ 7
 
0.5%
" 6
 
0.4%
! 4
 
0.3%
: 2
 
0.1%
# 1
 
0.1%
Decimal Number
ValueCountFrequency (%)
5 18
27.7%
7 11
16.9%
0 10
15.4%
2 7
 
10.8%
1 6
 
9.2%
4 5
 
7.7%
6 3
 
4.6%
3 3
 
4.6%
8 1
 
1.5%
9 1
 
1.5%
Space Separator
ValueCountFrequency (%)
4867
99.9%
4
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 257
100.0%
Close Punctuation
ValueCountFrequency (%)
) 255
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 176
100.0%
Math Symbol
ValueCountFrequency (%)
+ 31
100.0%
Final Punctuation
ValueCountFrequency (%)
2
100.0%
Format
ValueCountFrequency (%)
2
100.0%
Other Symbol
ValueCountFrequency (%)
2
100.0%
Currency Symbol
ValueCountFrequency (%)
£ 1
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 230619
97.0%
Common 7066
 
3.0%
Han 54
 
< 0.1%
Cyrillic 19
 
< 0.1%
Hebrew 5
 
< 0.1%
Hangul 4
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 30311
 
13.1%
e 22652
 
9.8%
i 19642
 
8.5%
n 19283
 
8.4%
r 13903
 
6.0%
l 12122
 
5.3%
o 9977
 
4.3%
s 7496
 
3.3%
t 7479
 
3.2%
h 7249
 
3.1%
Other values (70) 80505
34.9%
Common
ValueCountFrequency (%)
4867
68.9%
& 1015
 
14.4%
. 274
 
3.9%
( 257
 
3.6%
) 255
 
3.6%
- 176
 
2.5%
/ 38
 
0.5%
, 32
 
0.5%
+ 31
 
0.4%
' 23
 
0.3%
Other values (22) 98
 
1.4%
Han
ValueCountFrequency (%)
6
 
11.1%
5
 
9.3%
5
 
9.3%
5
 
9.3%
3
 
5.6%
3
 
5.6%
2
 
3.7%
2
 
3.7%
1
 
1.9%
1
 
1.9%
Other values (21) 21
38.9%
Cyrillic
ValueCountFrequency (%)
н 3
15.8%
а 3
15.8%
А 2
10.5%
Т 2
10.5%
и 2
10.5%
р 1
 
5.3%
л 1
 
5.3%
е 1
 
5.3%
к 1
 
5.3%
с 1
 
5.3%
Other values (2) 2
10.5%
Hebrew
ValueCountFrequency (%)
ד 1
20.0%
נ 1
20.0%
י 1
20.0%
א 1
20.0%
ל 1
20.0%
Hangul
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 237472
99.9%
None 203
 
0.1%
CJK 54
 
< 0.1%
Cyrillic 19
 
< 0.1%
Punctuation 8
 
< 0.1%
Hebrew 5
 
< 0.1%
Hangul 4
 
< 0.1%
Misc Symbols 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 30311
 
12.8%
e 22652
 
9.5%
i 19642
 
8.3%
n 19283
 
8.1%
r 13903
 
5.9%
l 12122
 
5.1%
o 9977
 
4.2%
s 7496
 
3.2%
t 7479
 
3.1%
h 7249
 
3.1%
Other values (69) 87358
36.8%
None
ValueCountFrequency (%)
é 84
41.4%
í 18
 
8.9%
ú 18
 
8.9%
á 17
 
8.4%
ë 11
 
5.4%
ô 9
 
4.4%
ó 8
 
3.9%
è 6
 
3.0%
ç 5
 
2.5%
ï 4
 
2.0%
Other values (19) 23
 
11.3%
CJK
ValueCountFrequency (%)
6
 
11.1%
5
 
9.3%
5
 
9.3%
5
 
9.3%
3
 
5.6%
3
 
5.6%
2
 
3.7%
2
 
3.7%
1
 
1.9%
1
 
1.9%
Other values (21) 21
38.9%
Punctuation
ValueCountFrequency (%)
4
50.0%
2
25.0%
2
25.0%
Cyrillic
ValueCountFrequency (%)
н 3
15.8%
а 3
15.8%
А 2
10.5%
Т 2
10.5%
и 2
10.5%
р 1
 
5.3%
л 1
 
5.3%
е 1
 
5.3%
к 1
 
5.3%
с 1
 
5.3%
Other values (2) 2
10.5%
Misc Symbols
ValueCountFrequency (%)
2
100.0%
Hangul
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Hebrew
ValueCountFrequency (%)
ד 1
20.0%
נ 1
20.0%
י 1
20.0%
א 1
20.0%
ל 1
20.0%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size606.6 KiB
Manhattan
16621 
Brooklyn
16439 
Queens
4572 
Bronx
 
875
Staten Island
 
314

Length

Max length13
Median length9
Mean length8.1654259
Min length5

Characters and Unicode

Total characters316990
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBrooklyn
2nd rowManhattan
3rd rowBrooklyn
4th rowManhattan
5th rowManhattan

Common Values

ValueCountFrequency (%)
Manhattan 16621
42.8%
Brooklyn 16439
42.3%
Queens 4572
 
11.8%
Bronx 875
 
2.3%
Staten Island 314
 
0.8%

Length

2023-02-26T17:25:11.844389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-26T17:25:11.941845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
manhattan 16621
42.5%
brooklyn 16439
42.0%
queens 4572
 
11.7%
bronx 875
 
2.2%
staten 314
 
0.8%
island 314
 
0.8%

Most occurring characters

ValueCountFrequency (%)
n 55756
17.6%
a 50491
15.9%
t 33870
10.7%
o 33753
10.6%
B 17314
 
5.5%
r 17314
 
5.5%
l 16753
 
5.3%
M 16621
 
5.2%
h 16621
 
5.2%
y 16439
 
5.2%
Other values (10) 42058
13.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 277541
87.6%
Uppercase Letter 39135
 
12.3%
Space Separator 314
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 55756
20.1%
a 50491
18.2%
t 33870
12.2%
o 33753
12.2%
r 17314
 
6.2%
l 16753
 
6.0%
h 16621
 
6.0%
y 16439
 
5.9%
k 16439
 
5.9%
e 9458
 
3.4%
Other values (4) 10647
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
B 17314
44.2%
M 16621
42.5%
Q 4572
 
11.7%
S 314
 
0.8%
I 314
 
0.8%
Space Separator
ValueCountFrequency (%)
314
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 316676
99.9%
Common 314
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 55756
17.6%
a 50491
15.9%
t 33870
10.7%
o 33753
10.7%
B 17314
 
5.5%
r 17314
 
5.5%
l 16753
 
5.3%
M 16621
 
5.2%
h 16621
 
5.2%
y 16439
 
5.2%
Other values (9) 41744
13.2%
Common
ValueCountFrequency (%)
314
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 316990
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 55756
17.6%
a 50491
15.9%
t 33870
10.7%
o 33753
10.6%
B 17314
 
5.5%
r 17314
 
5.5%
l 16753
 
5.3%
M 16621
 
5.2%
h 16621
 
5.2%
y 16439
 
5.2%
Other values (10) 42058
13.3%

neighbourhood
Categorical

Distinct218
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size606.6 KiB
Williamsburg
3163 
Bedford-Stuyvesant
3141 
Harlem
 
2204
Bushwick
 
1942
Hell's Kitchen
 
1528
Other values (213)
26843 

Length

Max length26
Median length17
Mean length11.928235
Min length4

Characters and Unicode

Total characters463066
Distinct characters54
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowKensington
2nd rowMidtown
3rd rowClinton Hill
4th rowEast Harlem
5th rowMurray Hill

Common Values

ValueCountFrequency (%)
Williamsburg 3163
 
8.1%
Bedford-Stuyvesant 3141
 
8.1%
Harlem 2204
 
5.7%
Bushwick 1942
 
5.0%
Hell's Kitchen 1528
 
3.9%
East Village 1489
 
3.8%
Upper West Side 1482
 
3.8%
Upper East Side 1405
 
3.6%
Crown Heights 1265
 
3.3%
Midtown 986
 
2.5%
Other values (208) 20216
52.1%

Length

2023-02-26T17:25:12.040161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
east 5374
 
8.6%
side 3625
 
5.8%
williamsburg 3163
 
5.0%
harlem 3147
 
5.0%
bedford-stuyvesant 3141
 
5.0%
heights 2889
 
4.6%
upper 2887
 
4.6%
village 2522
 
4.0%
west 2110
 
3.4%
bushwick 1942
 
3.1%
Other values (231) 31858
50.8%

Most occurring characters

ValueCountFrequency (%)
e 42598
 
9.2%
i 32780
 
7.1%
s 31811
 
6.9%
t 30583
 
6.6%
a 30156
 
6.5%
l 27652
 
6.0%
r 26841
 
5.8%
23837
 
5.1%
n 20721
 
4.5%
o 19240
 
4.2%
Other values (44) 176847
38.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 367305
79.3%
Uppercase Letter 66693
 
14.4%
Space Separator 23837
 
5.1%
Dash Punctuation 3592
 
0.8%
Other Punctuation 1639
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 42598
11.6%
i 32780
 
8.9%
s 31811
 
8.7%
t 30583
 
8.3%
a 30156
 
8.2%
l 27652
 
7.5%
r 26841
 
7.3%
n 20721
 
5.6%
o 19240
 
5.2%
d 15627
 
4.3%
Other values (15) 89296
24.3%
Uppercase Letter
ValueCountFrequency (%)
H 9622
14.4%
S 9306
14.0%
B 6776
10.2%
W 6470
9.7%
E 5790
8.7%
C 4256
 
6.4%
G 2991
 
4.5%
U 2933
 
4.4%
F 2597
 
3.9%
V 2557
 
3.8%
Other values (14) 13395
20.1%
Other Punctuation
ValueCountFrequency (%)
' 1533
93.5%
. 104
 
6.3%
, 2
 
0.1%
Space Separator
ValueCountFrequency (%)
23837
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3592
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 433998
93.7%
Common 29068
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 42598
 
9.8%
i 32780
 
7.6%
s 31811
 
7.3%
t 30583
 
7.0%
a 30156
 
6.9%
l 27652
 
6.4%
r 26841
 
6.2%
n 20721
 
4.8%
o 19240
 
4.4%
d 15627
 
3.6%
Other values (39) 155989
35.9%
Common
ValueCountFrequency (%)
23837
82.0%
- 3592
 
12.4%
' 1533
 
5.3%
. 104
 
0.4%
, 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 463066
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 42598
 
9.2%
i 32780
 
7.1%
s 31811
 
6.9%
t 30583
 
6.6%
a 30156
 
6.5%
l 27652
 
6.0%
r 26841
 
5.8%
23837
 
5.1%
n 20721
 
4.5%
o 19240
 
4.2%
Other values (44) 176847
38.2%

latitude
Real number (ℝ)

Distinct17436
Distinct (%)44.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.728129
Minimum40.50641
Maximum40.91306
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size606.6 KiB
2023-02-26T17:25:12.148361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum40.50641
5-th percentile40.64567
Q140.68864
median40.72171
Q340.76299
95-th percentile40.82605
Maximum40.91306
Range0.40665
Interquartile range (IQR)0.07435

Descriptive statistics

Standard deviation0.054990741
Coefficient of variation (CV)0.0013501907
Kurtosis0.095032403
Mean40.728129
Median Absolute Deviation (MAD)0.03641
Skewness0.2725179
Sum1581106.7
Variance0.0030239816
MonotonicityNot monotonic
2023-02-26T17:25:12.252057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.71813 15
 
< 0.1%
40.69414 11
 
< 0.1%
40.68634 11
 
< 0.1%
40.71353 11
 
< 0.1%
40.71515 10
 
< 0.1%
40.68374 10
 
< 0.1%
40.67757 10
 
< 0.1%
40.68444 10
 
< 0.1%
40.69054 10
 
< 0.1%
40.71947 10
 
< 0.1%
Other values (17426) 38713
99.7%
ValueCountFrequency (%)
40.50641 1
< 0.1%
40.50868 1
< 0.1%
40.50873 1
< 0.1%
40.50943 1
< 0.1%
40.51133 1
< 0.1%
40.52211 1
< 0.1%
40.52293 1
< 0.1%
40.53871 1
< 0.1%
40.53982 1
< 0.1%
40.53987 1
< 0.1%
ValueCountFrequency (%)
40.91306 1
< 0.1%
40.91234 1
< 0.1%
40.91167 1
< 0.1%
40.90804 1
< 0.1%
40.90484 1
< 0.1%
40.90406 1
< 0.1%
40.90391 1
< 0.1%
40.90356 1
< 0.1%
40.90329 1
< 0.1%
40.90175 1
< 0.1%

longitude
Real number (ℝ)

Distinct13639
Distinct (%)35.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-73.951149
Minimum-74.24442
Maximum-73.71299
Zeros0
Zeros (%)0.0%
Negative38821
Negative (%)100.0%
Memory size606.6 KiB
2023-02-26T17:25:12.361877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-74.24442
5-th percentile-74.00329
Q1-73.98246
median-73.95481
Q3-73.93502
95-th percentile-73.86217
Maximum-73.71299
Range0.53143
Interquartile range (IQR)0.04744

Descriptive statistics

Standard deviation0.046692964
Coefficient of variation (CV)-0.00063140282
Kurtosis4.8048546
Mean-73.951149
Median Absolute Deviation (MAD)0.02492
Skewness1.2562663
Sum-2870857.5
Variance0.0021802329
MonotonicityNot monotonic
2023-02-26T17:25:12.467936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-73.95427 16
 
< 0.1%
-73.95677 16
 
< 0.1%
-73.95405 15
 
< 0.1%
-73.95551 14
 
< 0.1%
-73.95742 14
 
< 0.1%
-73.9506 14
 
< 0.1%
-73.95688 13
 
< 0.1%
-73.943 13
 
< 0.1%
-73.9398 13
 
< 0.1%
-73.95669 13
 
< 0.1%
Other values (13629) 38680
99.6%
ValueCountFrequency (%)
-74.24442 1
< 0.1%
-74.23986 1
< 0.1%
-74.23914 1
< 0.1%
-74.23803 1
< 0.1%
-74.23059 1
< 0.1%
-74.21238 1
< 0.1%
-74.21017 1
< 0.1%
-74.19626 1
< 0.1%
-74.18259 1
< 0.1%
-74.18028 1
< 0.1%
ValueCountFrequency (%)
-73.71299 1
< 0.1%
-73.71928 1
< 0.1%
-73.72179 1
< 0.1%
-73.72435 1
< 0.1%
-73.72581 1
< 0.1%
-73.72582 1
< 0.1%
-73.72597 1
< 0.1%
-73.726 1
< 0.1%
-73.72656 1
< 0.1%
-73.72716 1
< 0.1%

room_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size606.6 KiB
Entire home/apt
20321 
Private room
17654 
Shared room
 
846

Length

Max length15
Median length15
Mean length13.548569
Min length11

Characters and Unicode

Total characters525969
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrivate room
2nd rowEntire home/apt
3rd rowEntire home/apt
4th rowEntire home/apt
5th rowEntire home/apt

Common Values

ValueCountFrequency (%)
Entire home/apt 20321
52.3%
Private room 17654
45.5%
Shared room 846
 
2.2%

Length

2023-02-26T17:25:12.561459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-26T17:25:12.652003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
entire 20321
26.2%
home/apt 20321
26.2%
room 18500
23.8%
private 17654
22.7%
shared 846
 
1.1%

Most occurring characters

ValueCountFrequency (%)
e 59142
11.2%
t 58296
11.1%
o 57321
10.9%
r 57321
10.9%
a 38821
 
7.4%
38821
 
7.4%
m 38821
 
7.4%
i 37975
 
7.2%
h 21167
 
4.0%
p 20321
 
3.9%
Other values (7) 97963
18.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 428006
81.4%
Space Separator 38821
 
7.4%
Uppercase Letter 38821
 
7.4%
Other Punctuation 20321
 
3.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 59142
13.8%
t 58296
13.6%
o 57321
13.4%
r 57321
13.4%
a 38821
9.1%
m 38821
9.1%
i 37975
8.9%
h 21167
 
4.9%
p 20321
 
4.7%
n 20321
 
4.7%
Other values (2) 18500
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
E 20321
52.3%
P 17654
45.5%
S 846
 
2.2%
Space Separator
ValueCountFrequency (%)
38821
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 20321
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 466827
88.8%
Common 59142
 
11.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 59142
12.7%
t 58296
12.5%
o 57321
12.3%
r 57321
12.3%
a 38821
8.3%
m 38821
8.3%
i 37975
8.1%
h 21167
 
4.5%
p 20321
 
4.4%
E 20321
 
4.4%
Other values (5) 57321
12.3%
Common
ValueCountFrequency (%)
38821
65.6%
/ 20321
34.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 525969
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 59142
11.2%
t 58296
11.1%
o 57321
10.9%
r 57321
10.9%
a 38821
 
7.4%
38821
 
7.4%
m 38821
 
7.4%
i 37975
 
7.2%
h 21167
 
4.0%
p 20321
 
3.9%
Other values (7) 97963
18.6%

price
Real number (ℝ)

Distinct581
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean142.33253
Minimum0
Maximum10000
Zeros10
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size606.6 KiB
2023-02-26T17:25:12.741913image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile40
Q169
median101
Q3170
95-th percentile330
Maximum10000
Range10000
Interquartile range (IQR)101

Descriptive statistics

Standard deviation196.99476
Coefficient of variation (CV)1.3840459
Kurtosis953.48074
Mean142.33253
Median Absolute Deviation (MAD)45
Skewness23.673594
Sum5525491
Variance38806.934
MonotonicityNot monotonic
2023-02-26T17:25:12.841866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 1596
 
4.1%
100 1517
 
3.9%
50 1188
 
3.1%
60 1155
 
3.0%
75 1095
 
2.8%
200 1025
 
2.6%
80 1022
 
2.6%
65 1006
 
2.6%
70 938
 
2.4%
120 921
 
2.4%
Other values (571) 27358
70.5%
ValueCountFrequency (%)
0 10
< 0.1%
10 12
< 0.1%
11 1
 
< 0.1%
12 1
 
< 0.1%
13 1
 
< 0.1%
15 3
 
< 0.1%
16 5
 
< 0.1%
18 1
 
< 0.1%
19 4
 
< 0.1%
20 23
0.1%
ValueCountFrequency (%)
10000 2
 
< 0.1%
9999 2
 
< 0.1%
8500 1
 
< 0.1%
8000 1
 
< 0.1%
7500 1
 
< 0.1%
6000 1
 
< 0.1%
5100 1
 
< 0.1%
5000 5
< 0.1%
4500 1
 
< 0.1%
3900 1
 
< 0.1%

minimum_nights
Real number (ℝ)

Distinct89
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8692203
Minimum1
Maximum1250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size606.6 KiB
2023-02-26T17:25:12.948358image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile30
Maximum1250
Range1249
Interquartile range (IQR)3

Descriptive statistics

Standard deviation17.389026
Coefficient of variation (CV)2.962749
Kurtosis1381.6227
Mean5.8692203
Median Absolute Deviation (MAD)1
Skewness27.542187
Sum227849
Variance302.37823
MonotonicityNot monotonic
2023-02-26T17:25:13.074331image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 10300
26.5%
1 9878
25.4%
3 6840
17.6%
4 2749
 
7.1%
5 2425
 
6.2%
30 2300
 
5.9%
7 1453
 
3.7%
6 612
 
1.6%
14 380
 
1.0%
10 343
 
0.9%
Other values (79) 1541
 
4.0%
ValueCountFrequency (%)
1 9878
25.4%
2 10300
26.5%
3 6840
17.6%
4 2749
 
7.1%
5 2425
 
6.2%
6 612
 
1.6%
7 1453
 
3.7%
8 102
 
0.3%
9 62
 
0.2%
10 343
 
0.9%
ValueCountFrequency (%)
1250 1
 
< 0.1%
999 2
 
< 0.1%
500 2
 
< 0.1%
370 1
 
< 0.1%
365 19
< 0.1%
364 1
 
< 0.1%
360 1
 
< 0.1%
300 3
 
< 0.1%
270 2
 
< 0.1%
265 1
 
< 0.1%

number_of_reviews
Real number (ℝ)

Distinct393
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.290255
Minimum1
Maximum629
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size606.6 KiB
2023-02-26T17:25:13.202273image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median9
Q333
95-th percentile129
Maximum629
Range628
Interquartile range (IQR)30

Descriptive statistics

Standard deviation48.1829
Coefficient of variation (CV)1.6450147
Kurtosis15.966318
Mean29.290255
Median Absolute Deviation (MAD)8
Skewness3.325293
Sum1137077
Variance2321.5918
MonotonicityNot monotonic
2023-02-26T17:25:13.296725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 5239
 
13.5%
2 3464
 
8.9%
3 2519
 
6.5%
4 1994
 
5.1%
5 1615
 
4.2%
6 1356
 
3.5%
7 1179
 
3.0%
8 1127
 
2.9%
9 964
 
2.5%
10 803
 
2.1%
Other values (383) 18561
47.8%
ValueCountFrequency (%)
1 5239
13.5%
2 3464
8.9%
3 2519
6.5%
4 1994
 
5.1%
5 1615
 
4.2%
6 1356
 
3.5%
7 1179
 
3.0%
8 1127
 
2.9%
9 964
 
2.5%
10 803
 
2.1%
ValueCountFrequency (%)
629 1
< 0.1%
607 1
< 0.1%
597 1
< 0.1%
594 1
< 0.1%
576 1
< 0.1%
543 1
< 0.1%
540 1
< 0.1%
510 1
< 0.1%
488 1
< 0.1%
480 1
< 0.1%

last_review
Categorical

Distinct1764
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size606.6 KiB
23-06-2019
 
1413
01-07-2019
 
1359
30-06-2019
 
1341
24-06-2019
 
875
07-07-2019
 
717
Other values (1759)
33116 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters388210
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique236 ?
Unique (%)0.6%

Sample

1st row19-10-2018
2nd row21-05-2019
3rd row05-07-2019
4th row19-11-2018
5th row22-06-2019

Common Values

ValueCountFrequency (%)
23-06-2019 1413
 
3.6%
01-07-2019 1359
 
3.5%
30-06-2019 1341
 
3.5%
24-06-2019 875
 
2.3%
07-07-2019 717
 
1.8%
02-07-2019 657
 
1.7%
22-06-2019 655
 
1.7%
16-06-2019 601
 
1.5%
05-07-2019 580
 
1.5%
06-07-2019 565
 
1.5%
Other values (1754) 30058
77.4%

Length

2023-02-26T17:25:13.383245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
23-06-2019 1413
 
3.6%
01-07-2019 1359
 
3.5%
30-06-2019 1341
 
3.5%
24-06-2019 875
 
2.3%
07-07-2019 717
 
1.8%
02-07-2019 657
 
1.7%
22-06-2019 655
 
1.7%
16-06-2019 601
 
1.5%
05-07-2019 580
 
1.5%
06-07-2019 565
 
1.5%
Other values (1754) 30058
77.4%

Most occurring characters

ValueCountFrequency (%)
0 92276
23.8%
- 77642
20.0%
1 61982
16.0%
2 58655
15.1%
9 30096
 
7.8%
6 19883
 
5.1%
7 12817
 
3.3%
8 10828
 
2.8%
5 9569
 
2.5%
3 8762
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 310568
80.0%
Dash Punctuation 77642
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 92276
29.7%
1 61982
20.0%
2 58655
18.9%
9 30096
 
9.7%
6 19883
 
6.4%
7 12817
 
4.1%
8 10828
 
3.5%
5 9569
 
3.1%
3 8762
 
2.8%
4 5700
 
1.8%
Dash Punctuation
ValueCountFrequency (%)
- 77642
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 388210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 92276
23.8%
- 77642
20.0%
1 61982
16.0%
2 58655
15.1%
9 30096
 
7.8%
6 19883
 
5.1%
7 12817
 
3.3%
8 10828
 
2.8%
5 9569
 
2.5%
3 8762
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 388210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 92276
23.8%
- 77642
20.0%
1 61982
16.0%
2 58655
15.1%
9 30096
 
7.8%
6 19883
 
5.1%
7 12817
 
3.3%
8 10828
 
2.8%
5 9569
 
2.5%
3 8762
 
2.3%

reviews_per_month
Real number (ℝ)

Distinct937
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3732292
Minimum0.01
Maximum58.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size606.6 KiB
2023-02-26T17:25:13.465940image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.04
Q10.19
median0.72
Q32.02
95-th percentile4.64
Maximum58.5
Range58.49
Interquartile range (IQR)1.83

Descriptive statistics

Standard deviation1.6803278
Coefficient of variation (CV)1.2236324
Kurtosis42.528736
Mean1.3732292
Median Absolute Deviation (MAD)0.62
Skewness3.1315634
Sum53310.13
Variance2.8235014
MonotonicityNot monotonic
2023-02-26T17:25:13.561234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02 914
 
2.4%
0.05 892
 
2.3%
1 892
 
2.3%
0.03 804
 
2.1%
0.16 667
 
1.7%
0.04 655
 
1.7%
0.08 596
 
1.5%
0.09 592
 
1.5%
0.06 579
 
1.5%
0.11 539
 
1.4%
Other values (927) 31691
81.6%
ValueCountFrequency (%)
0.01 42
 
0.1%
0.02 914
2.4%
0.03 804
2.1%
0.04 655
1.7%
0.05 892
2.3%
0.06 579
1.5%
0.07 465
1.2%
0.08 596
1.5%
0.09 592
1.5%
0.1 457
1.2%
ValueCountFrequency (%)
58.5 1
< 0.1%
27.95 1
< 0.1%
20.94 1
< 0.1%
19.75 1
< 0.1%
17.82 1
< 0.1%
16.81 1
< 0.1%
16.22 1
< 0.1%
16.03 1
< 0.1%
15.78 1
< 0.1%
15.32 1
< 0.1%
Distinct47
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1666109
Minimum1
Maximum327
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size606.6 KiB
2023-02-26T17:25:13.655953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile9
Maximum327
Range326
Interquartile range (IQR)1

Descriptive statistics

Standard deviation26.302954
Coefficient of variation (CV)5.0909494
Kurtosis121.90908
Mean5.1666109
Median Absolute Deviation (MAD)0
Skewness10.628207
Sum200573
Variance691.8454
MonotonicityNot monotonic
2023-02-26T17:25:13.749651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
1 25334
65.3%
2 5757
 
14.8%
3 2494
 
6.4%
4 1282
 
3.3%
5 736
 
1.9%
6 476
 
1.2%
7 318
 
0.8%
8 317
 
0.8%
327 207
 
0.5%
9 187
 
0.5%
Other values (37) 1713
 
4.4%
ValueCountFrequency (%)
1 25334
65.3%
2 5757
 
14.8%
3 2494
 
6.4%
4 1282
 
3.3%
5 736
 
1.9%
6 476
 
1.2%
7 318
 
0.8%
8 317
 
0.8%
9 187
 
0.5%
10 148
 
0.4%
ValueCountFrequency (%)
327 207
0.5%
232 28
 
0.1%
121 43
 
0.1%
103 51
 
0.1%
96 90
0.2%
91 79
 
0.2%
87 39
 
0.1%
65 1
 
< 0.1%
52 91
0.2%
50 40
 
0.1%

availability_365
Real number (ℝ)

Distinct366
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114.8863
Minimum0
Maximum365
Zeros12675
Zeros (%)32.6%
Negative0
Negative (%)0.0%
Memory size606.6 KiB
2023-02-26T17:25:13.851235image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median55
Q3229
95-th percentile356
Maximum365
Range365
Interquartile range (IQR)229

Descriptive statistics

Standard deviation129.52995
Coefficient of variation (CV)1.1274621
Kurtosis-1.0296509
Mean114.8863
Median Absolute Deviation (MAD)55
Skewness0.72321806
Sum4460001
Variance16778.008
MonotonicityNot monotonic
2023-02-26T17:25:13.956609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12675
32.6%
365 841
 
2.2%
1 367
 
0.9%
364 301
 
0.8%
5 291
 
0.7%
3 278
 
0.7%
89 257
 
0.7%
2 240
 
0.6%
90 221
 
0.6%
179 216
 
0.6%
Other values (356) 23134
59.6%
ValueCountFrequency (%)
0 12675
32.6%
1 367
 
0.9%
2 240
 
0.6%
3 278
 
0.7%
4 209
 
0.5%
5 291
 
0.7%
6 212
 
0.5%
7 187
 
0.5%
8 207
 
0.5%
9 162
 
0.4%
ValueCountFrequency (%)
365 841
2.2%
364 301
 
0.8%
363 165
 
0.4%
362 118
 
0.3%
361 90
 
0.2%
360 77
 
0.2%
359 99
 
0.3%
358 132
 
0.3%
357 78
 
0.2%
356 66
 
0.2%

Interactions

2023-02-26T17:25:09.443104image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:00.450484image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:01.419178image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:02.369747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:03.511137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:04.496339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:05.446001image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:06.380946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:07.488747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:08.453076image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:09.551832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:00.552017image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:01.518709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:02.472824image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:03.607667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:04.604873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:05.542531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:06.475988image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:07.586280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:08.549603image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:09.653371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:00.644412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:01.608236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:02.576078image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:03.699910image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:04.704969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:05.632552image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:06.569515image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:07.677807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:08.640133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:09.765575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:00.746940image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:01.708398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:02.681119image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:03.797438image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:04.805497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:05.733082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:06.672044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:07.785343image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:08.741605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:09.866273image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:00.839472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:01.800871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:02.783651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:03.888965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:04.894024image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:05.823612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:06.765576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:07.878338image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:08.834629image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:09.965892image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:00.932035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:01.888396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:02.879180image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:03.981494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:04.984323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:05.910139image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:07.014613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:07.970866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:08.935479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:10.069509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:01.031062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:01.980927image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:02.983240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:04.083026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:05.074851image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:06.003667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:07.110140image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:08.064924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:09.044337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:10.192819image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:01.128593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:02.076455image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:03.082508image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:04.185328image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:05.172378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:06.098701image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:07.206436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:08.163456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:09.147574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:10.293001image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:01.224121image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:02.173983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:03.183039image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:04.278596image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:05.263414image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:06.193231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:07.298605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:08.256982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:09.244623image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:10.392590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:01.322649image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:02.270514image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:03.286629image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:04.371800image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:05.355473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:06.286758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:07.392664image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:08.354969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:25:09.343259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-02-26T17:25:14.051377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
idhost_idlatitudelongitudepriceminimum_nightsnumber_of_reviewsreviews_per_monthcalculated_host_listings_countavailability_365neighbourhood_grouproom_type
id1.0000.566-0.0050.088-0.050-0.155-0.3030.3600.0910.0740.0630.078
host_id0.5661.0000.0390.130-0.097-0.185-0.1050.2680.1240.1310.1000.099
latitude-0.0050.0391.0000.0410.1180.017-0.025-0.023-0.011-0.0210.5370.110
longitude0.0880.1300.0411.000-0.433-0.1200.0700.1190.1060.0940.6530.148
price-0.050-0.0970.118-0.4331.0000.117-0.010-0.019-0.1640.0640.0140.023
minimum_nights-0.155-0.1850.017-0.1200.1171.000-0.168-0.289-0.0080.0150.0010.004
number_of_reviews-0.303-0.105-0.0250.070-0.010-0.1681.0000.7060.0830.2960.0260.023
reviews_per_month0.3600.268-0.0230.119-0.019-0.2890.7061.0000.1460.3920.0480.029
calculated_host_listings_count0.0910.124-0.0110.106-0.164-0.0080.0830.1461.0000.3690.0710.074
availability_3650.0740.131-0.0210.0940.0640.0150.2960.3920.3691.0000.0860.096
neighbourhood_group0.0630.1000.5370.6530.0140.0010.0260.0480.0710.0861.0000.111
room_type0.0780.0990.1100.1480.0230.0040.0230.0290.0740.0960.1111.000

Missing values

2023-02-26T17:25:10.552973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-26T17:25:10.786620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365
02539Clean & quiet apt home by the park2787JohnBrooklynKensington40.65-73.97Private room1491919-10-20180.216365
12595Skylit Midtown Castle2845JenniferManhattanMidtown40.75-73.98Entire home/apt22514521-05-20190.382355
33831Cozy Entire Floor of Brownstone4869LisaRoxanneBrooklynClinton Hill40.69-73.96Entire home/apt89127005-07-20194.641194
45022Entire Apt: Spacious Studio/Loft by central park7192LauraManhattanEast Harlem40.80-73.94Entire home/apt8010919-11-20180.1010
55099Large Cozy 1 BR Apartment In Midtown East7322ChrisManhattanMurray Hill40.75-73.97Entire home/apt20037422-06-20190.591129
65121BlissArtsSpace!7356GaronBrooklynBedford-Stuyvesant40.69-73.96Private room60454905-10-20170.4010
75178Large Furnished Room Near B'way8967ShunichiManhattanHell's Kitchen40.76-73.98Private room79243024-06-20193.471220
85203Cozy Clean Guest Room - Family Apt7490MaryEllenManhattanUpper West Side40.80-73.97Private room79211821-07-20170.9910
95238Cute & Cozy Lower East Side 1 bdrm7549BenManhattanChinatown40.71-73.99Entire home/apt150116009-06-20191.334188
105295Beautiful 1br on Upper West Side7702LenaManhattanUpper West Side40.80-73.97Entire home/apt13555322-06-20190.4316
idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365
4863436351128One bedroom without roomies, close to everything273361532David & AmyManhattanUpper West Side40.80-73.97Entire home/apt1103205-07-20192.00115
48636363515432-MONTH SUBLEASE (WITH EARLY MOVE-IN)52984497FridayBrooklynBedford-Stuyvesant40.69-73.92Private room3330206-07-20192.00187
4870136390226Comfortable clean Bedstuy private room267932490AngelaBrooklynBedford-Stuyvesant40.70-73.94Private room451208-07-20192.00114
4873236411407Brand new 1 bedroom steps from Soho!33917435MikeManhattanLower East Side40.72-73.99Entire home/apt1504106-07-20191.00113
4874036413632Spacious 2BR in Beautiful Brooklyn Heights6608220MattBrooklynBrooklyn Heights40.70-74.00Entire home/apt5503107-07-20191.001230
4878236425863Lovely Privet Bedroom with Privet Restroom83554966RusaaManhattanUpper East Side40.78-73.95Private room1291107-07-20191.001147
4879036427429No.2 with queen size bed257683179H AiQueensFlushing40.75-73.81Private room451107-07-20191.006339
4879936438336Seas The Moment211644523BenStaten IslandGreat Kills40.54-74.14Private room2351107-07-20191.00187
48805364422521B-1B apartment near by Metro273841667BlaineBronxMott Haven40.81-73.92Entire home/apt1001207-07-20192.00140
4885236455809Cozy Private Room in Bushwick, Brooklyn74162901ChristineBrooklynBushwick40.70-73.93Private room301108-07-20191.0011